Brian M. Powell

Bio: Brian M. Powell is an academic researcher from West Virginia University. The author has contributed to research in topics: CAPTCHA & Face detection. The author has an hindex of 5, co-authored 7 publications receiving 117 citations.

FaceDCAPTCHA: Face detection based color image CAPTCHA

Abstract: With data theft and computer break-ins becoming increasingly common, there is a great need for secondary authentication to reduce automated attacks while posing a minimal hindrance to legitimate users. CAPTCHA is one of the possible ways to classify human users and automated scripts. Though text-based CAPTCHAs are used in many applications, they pose a challenge due to language dependency. In this paper, we propose a face image-based CAPTCHA as a potential solution. To solve the CAPTCHA, users must correctly identify visually-distorted human faces embedded in a complex background without selecting any non-human faces. The proposed algorithm generates a CAPTCHA that offers better human accuracy and lower machine attack rates compared to existing approaches.

FR-CAPTCHA: CAPTCHA based on recognizing human faces.

Abstract: A Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is designed to distinguish humans from machines. Most of the existing tests require reading distorted text embedded in a background image. However, many existing CAPTCHAs are either too difficult for humans due to excessive distortions or are trivial for automated algorithms to solve. These CAPTCHAs also suffer from inherent language as well as alphabet dependencies and are not equally convenient for people of different demographics. Therefore, there is a need to devise other Turing tests which can mitigate these challenges. One such test is matching two faces to establish if they belong to the same individual or not. Utilizing face recognition as the Turing test, we propose FR-CAPTCHA based on finding matching pairs of human faces in an image. We observe that, compared to existing implementations, FR-CAPTCHA achieves a human accuracy of 94% and is robust against automated attacks.

Face recognition CAPTCHA

Abstract: CAPTCHA is one of the Turing tests used to classify human users and automated scripts. Existing CAPTCHAs, especially text-based CAPTCHAs, are used in many applications, however they pose challenges due to language dependency and high attack rates. In this paper, we propose a face recognition-based CAPTCHA as a potential solution. To solve the CAPTCHA, users must correctly find one pair of human face images, that belong to same subject, embedded in a complex background without selecting any nonface image or impostor pair. The proposed algorithm generates CAPTCHA that offer better human accuracy and lower attack rates compared to existing approaches.

fgCAPTCHA: Genetically Optimized Face Image CAPTCHA 5

Abstract: The increasing use of smartphones, tablets, and other mobile devices poses a significant challenge in providing effective online security. CAPTCHAs, tests for distinguishing human and computer users, have traditionally been popular; however, they face particular difficulties in a modern mobile environment because most of them rely on keyboard input and have language dependencies. This paper proposes a novel image-based CAPTCHA that combines the touch-based input methods favored by mobile devices with genetically optimized face detection tests to provide a solution that is simple for humans to solve, ready for worldwide use, and provides a high level of security by being resilient to automated computer attacks. In extensive testing involving over 2600 users and 40000 CAPTCHA tests, fg CAPTCHA demonstrates a very high human success rate while ensuring a 0% attack rate using three well-known face detection algorithms.

A multibiometrics-based CAPTCHA for improved online security

Abstract: CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart) have been a common tool for preventing unauthorized access to websites for over a decade, but increasingly sophisticated optical character recognition algorithms and attack strategies have rendered traditional CAPTCHAs insecure. In this paper, we propose a new CAPTCHA incorporating multiple biometric modalities. Users are asked to identify faces, eyes, and fingerprints in a complex composite image. With over 1,900 volunteers and 30,000+ attempts, the proposed approach achieves high human accuracy while being resistant to existing attacks on CAPTCHAs and to detection by state-of-the-art software.

MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.

Abstract: This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work.

I am Robot: (Deep) Learning to Break Semantic Image CAPTCHAs

Abstract: Since their inception, captchas have been widely used for preventing fraudsters from performing illicit actions. Nevertheless, economic incentives have resulted in an arms race, where fraudsters develop automated solvers and, in turn, captcha services tweak their design to break the solvers. Recent work, however, presented a generic attack that can be applied to any text-based captcha scheme. Fittingly, Google recently unveiled the latest version of reCaptcha. The goal of their new system is twofold, to minimize the effort for legitimate users, while requiring tasks that are more challenging to computers than text recognition. ReCaptcha is driven by an "advanced risk analysis system" that evaluates requests and selects the difficulty of the captcha that will be returned. Users may be required to click in a checkbox, or solve a challenge by identifying images with similar content. In this paper, we conduct a comprehensive study of reCaptcha, and explore how the risk analysis process is influenced by each aspect of the request. Through extensive experimentation, we identify flaws that allow adversaries to effortlessly influence the risk analysis, bypass restrictions, and deploy large-scale attacks. Subsequently, we design a novel low-cost attack that leverages deep learning technologies for the semantic annotation of images. Our system is extremely effective, automatically solving 70.78% of the image reCaptcha challenges, while requiring only 19 seconds per challenge. We also apply our attack to the Facebook image captcha and achieve an accuracy of 83.5%. Based on our experimental findings, we propose a series of safeguards and modifications for impacting the scalability and accuracy of our attacks. Overall, while our study focuses on reCaptcha, our findings have wide implications, as the semantic information conveyed via images is increasingly within the realm of automated reasoning, the future of captchas relies on the exploration of novel directions.

A highly efficient redundancy scheme: self-purging redundancy

Abstract: The goals of this paper are to present an efficient redundancy scheme for highly reliable systems, to give a method to compute the exact reliability of such schemes and to compare this scheme with other redundancy schemes. This redundancy scheme is self-purging redundancy; a scheme that uses a threshold voter and that purges the failed modules. Switches for self-purging systems are extremely simple: there is no replacement of failed modules and module purging is quite simply implemented. Because of switch simplicity, exact reliability calculations are possible. The effects of switch reliability are quantitatively examined. For short mission times, switch reliability is the most important factor: self-purging systems have a probability of failure several times larger than the figure obtained when switches are assumed to be perfect. The influence of the relative frequency of the diverse types of failures (permanent, intermittent, stuck-at,...) are also investigated. Reliability functions, mission time improvements and switch efficiency are displayed. Self-purging systems are compared with ot her redundant systems, like hybrid or NMR, for their relative merits in reliability gain, simplicity, cost and confidence in the reliability estimation. The high confidence in the reliability evaluation of self-purging systems makes them a standard for the validation of several models that have been proposed to take into account switch reliability. The accuracy of models using coverage factors can be evaluated that way.

Research on Deep Learning Techniques in Breaking Text-Based Captchas and Designing Image-Based Captcha

Abstract: The ability of hackers to infiltrate computer systems using computer attack programs and bots led to the development of Captchas or Completely Automated Public Turing Tests to Tell Computers and Humans Apart. The text Captcha is the most popular Captcha scheme given its ease of construction and user friendliness. However, the next generation of hackers and programmers has decreased the expected security of these mechanisms, leaving websites open to attack. Text Captchas are still widely used, because it is believed that the attack speeds are slow, typically two to five seconds per image, and this is not seen as a critical threat. In this paper, we introduce a simple, generic, and fast attack on text Captchas that effectively challenges that supposition. With deep learning techniques, our attack demonstrates a high success rate in breaking the Roman-character-based text Captchas deployed by the top 50 most popular international websites and three Chinese Captchas that use a larger character set. These targeted schemes cover almost all existing resistance mechanisms, demonstrating that our attack techniques are also applicable to other existing Captchas. Does this work then spell the beginning of the end for text-based Captcha? We believe so. A novel image-based Captcha named Style Area Captcha (SACaptcha) is proposed in this paper, which is based on semantic information understanding, pixel-level segmentation, and deep learning techniques. Having demonstrated that text Captchas are no longer secure, we hope that our proposal shows promise in the development of image-based Captchas using deep learning techniques.

FaceDCAPTCHA: Face detection based color image CAPTCHA

Abstract: With data theft and computer break-ins becoming increasingly common, there is a great need for secondary authentication to reduce automated attacks while posing a minimal hindrance to legitimate users. CAPTCHA is one of the possible ways to classify human users and automated scripts. Though text-based CAPTCHAs are used in many applications, they pose a challenge due to language dependency. In this paper, we propose a face image-based CAPTCHA as a potential solution. To solve the CAPTCHA, users must correctly identify visually-distorted human faces embedded in a complex background without selecting any non-human faces. The proposed algorithm generates a CAPTCHA that offers better human accuracy and lower machine attack rates compared to existing approaches.